What data formats/pipelining are best to store and wrangle data which contains both text and float vectors?
There are different possible ways to handle huge datasets:

If the data is too big to be fully uploaded to RAM, you can iterate over it in Pandas. You can find a brief explanation in the article Why and How to Use Pandas with Large Data, section 1. Read CSV file data in chunk size. Or add more RAM (or use powerful server hardware), if you want to continue using single machine.

If the data is really big, probably it's better to store and process it by multiple computers using special software, A specific tool depends on what you want to do with the data:



2.1. You can even keep using Pandas: there is an extension named Dask which wraps interfaces of Python iterables, NumPy, and Pandas to run on multiple machines. Also, there are other similar tools.
2.2. A more independent yes simple approach is to use some analytical, distributed DBMS like Google BigQuery or Cassandra.
2.3. And if you need something more powerful and complex, MapReduce systems like Apache Hadoop, Spark can be utilised. Moreover, there are many articles and scientific papers that describes usage of MapReduce for the machine learning.

So, as always, it is a matter of your opportunities and your aims of dealing with the data.